import scanpy as sc, anndata as ad, numpy as np, pandas as pd
from scipy import sparse
from anndata import AnnData
import warnings
import socket
import holoviews as hv
import plotly.express as px
from matplotlib import pylab
import sys
import yaml
import os
from pandas.api.types import CategoricalDtype
import plotly
import scvelo as scv
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
plotly.offline.init_notebook_mode()
import ipynbname
nb_fname = ipynbname.name()
%matplotlib inline
sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_header()
sc.settings.set_figure_params(dpi=50, facecolor='white')
pylab.rcParams['figure.figsize'] = (9, 9)
outBaseName = "04.1A_Neurons_DA"
figDir = "./figures"
scanpy==1.8.1 anndata==0.7.6 umap==0.4.6 numpy==1.20.2 scipy==1.6.3 pandas==1.2.4 scikit-learn==0.24.2 statsmodels==0.13.1 python-igraph==0.9.8 louvain==0.7.1 pynndescent==0.5.5
hostRoot = "-".join(socket.gethostname().split('-')[0:2])
with open(os.path.expanduser('~')+"/paths_config.yaml", 'r') as f:
paths = yaml.load(f, Loader=yaml.FullLoader)
#indir=paths["paths"]["indir"][hostRoot]
outdir="./outdir"
FinaLeaf="/Neurons"
#projectBaseDir=paths["paths"]["projectBaseDir"][hostRoot]
with open("./colorMap.yaml", 'r') as f:
colorMap = yaml.load(f, Loader=yaml.FullLoader)["uns_colors"]
colorMap
{'medial': {'color': '#CD5C5C'},
'distal': {'color': '#FFCBCB'},
'proximal': {'color': '#8D021F'},
'piece1': {'color': '#281E5D'},
'piece2': {'color': '#3779FF'},
'piece3': {'color': '#BFD4FF'},
'control': {'color': '#0056D1'},
'polaroid': {'color': '#DE001E'},
'enriched': {'color': '#DE001E'},
'not_enriched': {'color': '#0056D1'},
'pfc': {'color': '#DE001E'},
'somatosensory': {'color': '#E5E4E2'},
'temporal': {'color': '#0056D1'},
'motor': {'color': '#37F7C8'},
'v1': {'color': '#28F30C'},
'parietal': {'color': '#D41FFC'}}
adataAll = sc.read_h5ad(outdir+"/3_polaroid_quickAnno.h5ad")
adataAll
AnnData object with n_obs × n_vars = 18822 × 28371
obs: 'dataset', 'organoid', 'region', 'type', 'type_region', 'regionContrast', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo', 'n_genes', 'is.Stressed', 'leiden0.3', 'leidenAnno'
var: 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells', 'highly_variable', 'mean', 'std'
uns: 'dataset_colors', 'dendrogram_leidenAnno', 'diffmap_evals', 'draw_graph', 'is.Stressed_colors', 'leiden', 'leiden0.3_colors', 'leidenAnno_colors', 'neighbors', 'organoid_colors', 'pca', 'rank_genes_groups', 'regionContrast_colors', 'region_colors', 'type_colors', 'umap'
obsm: 'X_diffmap', 'X_draw_graph_fa', 'X_pca', 'X_umap'
varm: 'PCs'
obsp: 'connectivities', 'distances'
scv.tl.score_genes_cell_cycle(adataAll)
calculating cell cycle phase
computing score 'S_score'
finished: added
'S_score', score of gene set (adata.obs).
543 total control genes are used. (0:00:00)
computing score 'G2M_score'
finished: added
'G2M_score', score of gene set (adata.obs).
415 total control genes are used. (0:00:00)
--> 'S_score' and 'G2M_score', scores of cell cycle phases (adata.obs)
sc.pl.umap(adataAll, color=["leiden0.3","phase"] ,size = 30, add_outline = True,outline_width=(0.2, 0.05), frameon=False)
... storing 'phase' as categorical
sc.pl.umap(adataAll, color="leiden0.3", groups=["2","8"] ,size = 30, add_outline = True,outline_width=(0.2, 0.05), frameon=False)
adata = adataAll.raw.to_adata().copy()
del adata.var["highly_variable"]
adata.obs = adataAll[adata.obs_names].obs.copy()
del adata.uns
del adata.obsm
adata = adata[adata.obs["leiden0.3"].isin(["2","8"])]
for md in [md for md in adata.obs.columns if "leiden" in md and md != "leiden0.3"]:
del adata.obs[md]
adata.uns["regionContrast_colors"] = [colorMap[i]["color"] for i in adata.obs["regionContrast"].cat.categories]
adata.uns["region_colors"] = [colorMap[i]["color"] for i in adata.obs["region"].cat.categories]
adata.uns["type_colors"] = [colorMap[i]["color"] for i in adata.obs["type"].cat.categories]
Trying to set attribute `.uns` of view, copying.
adata
AnnData object with n_obs × n_vars = 3650 × 28371
obs: 'dataset', 'organoid', 'region', 'type', 'type_region', 'regionContrast', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo', 'n_genes', 'is.Stressed', 'leiden0.3', 'S_score', 'G2M_score', 'phase'
var: 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells'
uns: 'regionContrast_colors', 'region_colors', 'type_colors'
obsTupleToMap = ("region","leiden0.3")
SankeyDF=adata.obs[list(obsTupleToMap)]
SankeyDF.columns = ["region","leiden0.3"]
SankeyDF = SankeyDF.groupby(['region','leiden0.3']).size().reset_index().rename(columns={0:'count'})
SankeyDF=SankeyDF[SankeyDF["count"] != 0]
hv.extension('bokeh')
sankey1 = hv.Sankey(SankeyDF, kdims=["region", "leiden0.3"], vdims="count")
sankey1.opts(cmap='Colorblind',label_position='left',
edge_color='region', edge_line_width=0,
node_alpha=1.0, node_width=40, node_sort=True,
width=900, height=700, bgcolor="snow")
# Given the epxloratory phase as many genes were retained
VERTICAL_HVGs = {}
for group in adata.obs.type_region.unique():
adata_group = adata[adata.obs.type_region == group].copy()
HVGsDF = sc.pp.highly_variable_genes(adata_group, min_mean=0.0125, max_mean=3, min_disp=0.5, inplace=False, batch_key="organoid")
VERTICAL_HVGs[group] = set(HVGsDF[HVGsDF["highly_variable_nbatches"] == 3].index)
VERTICAL_HVGs = set.union(*list(VERTICAL_HVGs.values()))
extracting highly variable genes
finished (0:00:00)
extracting highly variable genes
finished (0:00:00)
extracting highly variable genes
finished (0:00:00)
extracting highly variable genes
finished (0:00:00)
extracting highly variable genes
finished (0:00:00)
extracting highly variable genes
finished (0:00:00)
len(VERTICAL_HVGs)
2693
import itertools
# Setting up contrasts
proximal = ["polaroid1_proximal","polaroid2_proximal","polaroid3_proximal"]
medial = ["polaroid1_medial","polaroid2_medial","polaroid3_medial"]
distal = ["polaroid1_distal","polaroid2_distal","polaroid3_distal"]
p1 = ["control1_piece1","control2_piece1","control3_piece1"]
p2 = ["control1_piece2","control2_piece2","control3_piece2"]
p3 = ["control1_piece3","control2_piece3","control3_piece3"]
proximal_vs_medial = list(itertools.product(proximal, medial))
medial_vs_distal = list(itertools.product(medial, distal))
p1_vs_p2 = list(itertools.product(p1, p2))
p2_vs_p3 = list(itertools.product(p2, p3))
HORIZONTAL_HVGs = {}
# Proximal vs distal regions
# Proximal vs distal regions
# Proximal vs distal regions
proximal_vs_medial_HVGs = {}
for contrast in proximal_vs_medial:
adataContrast = adata[adata.obs["dataset"].isin(list(contrast))].copy()
print(adataContrast.obs.dataset.value_counts())
sc.pp.highly_variable_genes(adataContrast, min_mean=0.0125, max_mean=3, min_disp=0.5)
HVGs = set(adataContrast.var[adataContrast.var.highly_variable].index.tolist())
proximal_vs_medial_HVGs["_".join(contrast)] = HVGs
proximal_vs_medial_HVGs = pd.Series(list(itertools.chain.from_iterable([list(i) for i in list(proximal_vs_medial_HVGs.values())])))
proximal_vs_medial_HVGs = set(proximal_vs_medial_HVGs.value_counts()[proximal_vs_medial_HVGs.value_counts() == 9].index.tolist())
HORIZONTAL_HVGs["proximal_vs_medial_HVGs"] = proximal_vs_medial_HVGs
# medial vs distal regions
# medial vs distal regions
# medial vs distal regions
medial_vs_distal_HVGs = {}
for contrast in medial_vs_distal:
adataContrast = adata[adata.obs["dataset"].isin(list(contrast))].copy()
print(adataContrast.obs.dataset.value_counts())
sc.pp.highly_variable_genes(adataContrast, min_mean=0.0125, max_mean=3, min_disp=0.5)
HVGs = set(adataContrast.var[adataContrast.var.highly_variable].index.tolist())
medial_vs_distal_HVGs["_".join(contrast)] = HVGs
medial_vs_distal_HVGs = pd.Series(list(itertools.chain.from_iterable([list(i) for i in list(medial_vs_distal_HVGs.values())])))
medial_vs_distal_HVGs = set(medial_vs_distal_HVGs.value_counts()[medial_vs_distal_HVGs.value_counts() == 9].index.tolist())
HORIZONTAL_HVGs["medial_vs_distal_HVGs"] = medial_vs_distal_HVGs
# P1 vs P2
# P1 vs P2
# P1 vs P2
p1_vs_p2_HVGs = {}
for contrast in p1_vs_p2:
adataContrast = adata[adata.obs["dataset"].isin(list(contrast))].copy()
print(adataContrast.obs.dataset.value_counts())
sc.pp.highly_variable_genes(adataContrast, min_mean=0.0125, max_mean=3, min_disp=0.5)
HVGs = set(adataContrast.var[adataContrast.var.highly_variable].index.tolist())
p1_vs_p2_HVGs["_".join(contrast)] = HVGs
p1_vs_p2_HVGs = pd.Series(list(itertools.chain.from_iterable([list(i) for i in list(p1_vs_p2_HVGs.values())])))
p1_vs_p2_HVGs = set(p1_vs_p2_HVGs.value_counts()[p1_vs_p2_HVGs.value_counts() == 9].index.tolist())
HORIZONTAL_HVGs["p1_vs_p2_HVGs"] = p1_vs_p2_HVGs
# P2 vs P3
# P2 vs P3
# P2 vs P3
p2_vs_p3_HVGs = {}
for contrast in p2_vs_p3:
adataContrast = adata[adata.obs["dataset"].isin(list(contrast))].copy()
print(adataContrast.obs.dataset.value_counts())
sc.pp.highly_variable_genes(adataContrast, min_mean=0.0125, max_mean=3, min_disp=0.5)
HVGs = set(adataContrast.var[adataContrast.var.highly_variable].index.tolist())
p2_vs_p3_HVGs["_".join(contrast)] = HVGs
p2_vs_p3_HVGs = pd.Series(list(itertools.chain.from_iterable([list(i) for i in list(p2_vs_p3_HVGs.values())])))
p2_vs_p3_HVGs = set(p2_vs_p3_HVGs.value_counts()[p2_vs_p3_HVGs.value_counts() == 9].index.tolist())
HORIZONTAL_HVGs["p2_vs_p3_HVGs"] = p2_vs_p3_HVGs
polaroid1_proximal 479
polaroid1_medial 148
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
polaroid1_proximal 479
polaroid2_medial 83
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
polaroid1_proximal 479
polaroid3_medial 43
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
polaroid2_proximal 350
polaroid1_medial 148
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
polaroid2_proximal 350
polaroid2_medial 83
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
polaroid2_proximal 350
polaroid3_medial 43
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
polaroid3_proximal 284
polaroid1_medial 148
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
polaroid3_proximal 284
polaroid2_medial 83
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
polaroid3_proximal 284
polaroid3_medial 43
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
polaroid1_distal 342
polaroid1_medial 148
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
polaroid1_medial 148
polaroid2_distal 83
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
polaroid1_medial 148
polaroid3_distal 63
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
polaroid1_distal 342
polaroid2_medial 83
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
polaroid2_distal 83
polaroid2_medial 83
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
polaroid2_medial 83
polaroid3_distal 63
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
polaroid1_distal 342
polaroid3_medial 43
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
polaroid2_distal 83
polaroid3_medial 43
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
polaroid3_distal 63
polaroid3_medial 43
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control1_piece2 110
control1_piece1 84
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control2_piece2 135
control1_piece1 84
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control3_piece2 300
control1_piece1 84
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control2_piece1 221
control1_piece2 110
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control2_piece1 221
control2_piece2 135
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control3_piece2 300
control2_piece1 221
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control3_piece1 281
control1_piece2 110
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control3_piece1 281
control2_piece2 135
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control3_piece2 300
control3_piece1 281
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control1_piece3 134
control1_piece2 110
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control2_piece3 139
control1_piece2 110
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control3_piece3 371
control1_piece2 110
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control2_piece2 135
control1_piece3 134
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control2_piece3 139
control2_piece2 135
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control3_piece3 371
control2_piece2 135
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control3_piece2 300
control1_piece3 134
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control3_piece2 300
control2_piece3 139
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
control3_piece3 371
control3_piece2 300
Name: dataset, dtype: int64
extracting highly variable genes
finished (0:00:00)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
HORIZONTAL_HVGs = set.union(*list(HORIZONTAL_HVGs.values()))
JointHVGs = list(HORIZONTAL_HVGs.union(VERTICAL_HVGs))
adata.var["highly_variable"] = adata.var_names.isin(JointHVGs)
adata.var["highly_variable"].sum()
2704
sc.pp.pca(adata, use_highly_variable=True)
computing PCA
on highly variable genes
with n_comps=50
finished (0:00:00)
sc.pp.neighbors(adata, n_neighbors=50, n_pcs=10)
computing neighbors
using 'X_pca' with n_pcs = 10
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:02)
sc.tl.leiden(adata, resolution=.4, key_added="subLeiden")
running Leiden clustering
finished: found 5 clusters and added
'subLeiden', the cluster labels (adata.obs, categorical) (0:00:00)
sc.tl.umap(adata)
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:00:06)
sc.tl.diffmap(adata)
computing Diffusion Maps using n_comps=15(=n_dcs)
computing transitions
finished (0:00:00)
eigenvalues of transition matrix
[1. 0.9754858 0.9639816 0.9497826 0.94473535 0.94054
0.92622113 0.9179847 0.9113687 0.9067488 0.90024513 0.8944447
0.88261294 0.87642825 0.86645806]
finished: added
'X_diffmap', diffmap coordinates (adata.obsm)
'diffmap_evals', eigenvalues of transition matrix (adata.uns) (0:00:00)
sc.tl.draw_graph(adata, n_jobs=4)
drawing single-cell graph using layout 'fa'
finished: added
'X_draw_graph_fa', graph_drawing coordinates (adata.obsm) (0:00:18)
sc.pl.umap(adata, color=["organoid","type","dataset","subLeiden","MKI67"], size = 100, add_outline = True,outline_width=(0.2, 0.05), ncols=3 ,
save=nb_fname+".UMAP.pdf")
WARNING: saving figure to file figures/umapNeurons01_Selection.UMAP.pdf
sc.pl.diffmap(adata, color=["organoid","type","dataset","subLeiden","MKI67","VIM","DCX","GAP43","PAX6","FOXG1","FGF8"], size = 50, add_outline = True,outline_width=(0.2, 0.05), ncols=3)
sc.pl.draw_graph(adata, color=["organoid","type","dataset","subLeiden","MKI67","VIM","DCX","GAP43","PAX6","FOXG1","FGF8"], size = 50, add_outline = True,outline_width=(0.2, 0.05), ncols=3)
sc.tl.embedding_density(adata, basis='umap', groupby='type')
sc.pl.embedding_density(adata, basis='umap', key='umap_density_type')
computing density on 'umap'
--> added
'umap_density_type', densities (adata.obs)
'umap_density_type_params', parameter (adata.uns)
sc.tl.embedding_density(adata, basis='umap', groupby='region')
sc.pl.embedding_density(adata, basis='umap', key='umap_density_region')
computing density on 'umap'
--> added
'umap_density_region', densities (adata.obs)
'umap_density_region_params', parameter (adata.uns)
sc.tl.embedding_density(adata, basis='umap', groupby='organoid')
sc.pl.embedding_density(adata, basis='umap', key='umap_density_organoid')
computing density on 'umap'
--> added
'umap_density_organoid', densities (adata.obs)
'umap_density_organoid_params', parameter (adata.uns)
adata.write_h5ad(outdir+FinaLeaf+"/4A_Neurons_DA.h5ad")